Recursive Optimal Mechanisms With History-Dependent, Privately-Observed Shocks
Christopher Phelan
No 292711, SSRI Workshop Series from University of Wisconsin-Madison, Social Systems Research Institute
Abstract:
This note extends the works of Spear and Srivastava (1987) and Phelan and Townsend (1991) on the use of recursive techniques for finding optimal mechanisms given repeated moral hazard. Specifically, it relaxes the assumption that the stochastic process determining privately observed shocks is time-independent, allowing instead such shocks to follow an n-stage Markov process. The techniques developed make transparent the fact that optimal mechanisms with history-dependent privately-observed shocks will, in general, be ex-post inefficient. The main trick is expand the set of state statistics from a singleton denoting the expected discounted utility of the agent at a point in time, to a vector of utilities denoting the expected discounted utility of the agent for each possible deviation strategy for the preceding n-periods. This method solves an ex-post adverse selection problem, and with a slight extension, an ex-ante adverse selection problem as well.
Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 14
Date: 1991-08
References: Add references at CitEc
Citations:
Downloads: (external link)
https://ageconsearch.umn.edu/record/292711/files/uwmad-0061.PDF (application/pdf)
Related works:
Working Paper: Recursive Optimal Mechanisms with History-Dependent, Privately-Observed Shocks (1991)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ags:uwssri:292711
DOI: 10.22004/ag.econ.292711
Access Statistics for this paper
More papers in SSRI Workshop Series from University of Wisconsin-Madison, Social Systems Research Institute Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().